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Found 15 papers, 1 papers with code
LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features.
Research on Financial Multi-Asset Portfolio Risk Prediction Model Based on Convolutional Neural Networks and Image Processing
In today's complex and volatile financial market environment, risk management of multi-asset portfolios faces significant challenges.
Visualizing Loss Functions as Topological Landscape Profiles
After describing this new topological landscape profile representation, we show how the shape of loss landscapes can reveal new details about model performance and learning dynamics, highlighting several use cases, including image segmentation (e. g., UNet) and scientific machine learning (e. g., physics-informed neural networks).
Enhancing Skin Lesion Diagnosis with Ensemble Learning
To further enhance classification accuracy, we developed ensemble models employing max voting, average voting, and stacking, resulting in accuracies of 0. 803, 0. 82, and 0. 83.
PoliPrompt: A High-Performance Cost-Effective LLM-Based Text Classification Framework for Political Science
Recent advancements in large language models (LLMs) have opened new avenues for enhancing text classification efficiency in political science, surpassing traditional machine learning methods that often require extensive feature engineering, human labeling, and task-specific training.
ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments
In this study, we examine the efficacy of post-hoc local attribution methods in identifying features with predictive power from irrelevant ones in domains characterized by a low signal-to-noise ratio (SNR), a common scenario in real-world machine learning applications.
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Knowledge Guided Entity-aware Video Captioning and A Basketball Benchmark
We develop a knowledge guided entity-aware video captioning network (KEANet) based on a candidate player list in encoder-decoder form for basketball live text broadcast.
A New Baseline Assumption of Integated Gradients Based on Shaply value
The selection of appropriate baselines in IG is crucial for crafting meaningful and unbiased explanations of model predictions in diverse settings.
Boosting Event Extraction with Denoised Structure-to-Text Augmentation
Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations.
Knowledge Augmented Relation Inference for Group Activity Recognition
Specifically, the framework consists of a Visual Representation Module to extract individual appearance features, a Knowledge Augmented Semantic Relation Module explore semantic representations of individual actions, and a Knowledge-Semantic-Visual Interaction Module aims to integrate visual and semantic information by the knowledge.
Dynamic Prefix-Tuning for Generative Template-based Event Extraction
We consider event extraction in a generative manner with template-based conditional generation.
Learning to Compose Diversified Prompts for Image Emotion Classification
We first introduce a prompt tuning method that mimics the pretraining objective of CLIP and thus can leverage the rich image and text semantics entailed in CLIP.
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction
Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions.
